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公开(公告)号：US10510022B1
公开(公告)日：2019-12-17
申请号：US16451228
申请日：2019-06-25
Applicant: SAS Institute Inc.
Inventor： Ricky Dee Tharrington, Jr. , Xin Jiang Hunt , Ralph Walter Abbey
IPC: G06N20/00 , G06N5/04 , G06F17/16 , G06F16/245
Abstract: Systems and methods for machine learning, models, and related explainability and interpretability are provided. A computing device determines a contribution of a feature to a predicted value. A feature computation dataset is defined based on a selected next selection vector. A prediction value is computed for each observation vector included in the feature computation dataset using a trained predictive model. An expected value is computed for the selected next selection vector based on the prediction values. The feature computation dataset is at least a partial copy of a training dataset with each variable value replaced in each observation vector included in the feature computation dataset based on the selected next selection vector. Each replaced variable value is replaced with a value included in a predefined query for a respective variable. A Shapley estimate value is computed for each variable.
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2.发明授权公开(公告)号：US12277410B1
公开(公告)日：2025-04-15
申请号：US19000671
申请日：2024-12-23
Applicant: SAS Institute Inc.
Inventor： Mohammadreza Nazari , Xindian Long , Steven Eric Krueger , Joshua David Griffin , Lawrence Edmund Lewis , Amirhassan Fallah Dizche , Ralph Walter Abbey , Jorge Manuel Gomes Da Silva
Abstract: A system, method, and computer-program product includes commencing a parent computer process based on receiving a request to perform an analytical operation on one or more datasets, commencing at least one child computer process that is launched by the parent computer process when the parent computer process initiates an execution of the analytical operation on the one or more datasets, transmitting, by the at least one child computer process, a request to the parent computer process to retrieve the one or more datasets, writing, by the parent computer process, the one or more datasets to a cross-process queue based on the parent computer process receiving the requests, reading, by the at least one child computer process, the one or more datasets from the cross-process queue, and executing, using an analytical application executing on the least one child computer process, the analytical operation based on the one or more datasets.
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公开(公告)号：US12093826B2
公开(公告)日：2024-09-17
申请号：US18444906
申请日：2024-02-19
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Ricky Dee Tharrington, Jr. , Ralph Walter Abbey , Xin Jiang Hunt
Abstract: A computing device trains a fair prediction model while defining an optimal event cutoff value. (A) A prediction model is trained with observation vectors. (B) The prediction model is executed to define a predicted target variable value and a probability associated with an accuracy of the predicted target variable value. (C) A conditional moments matrix is computed based on fairness constraints, the predicted target variable value, and the sensitive attribute variable value of each observation vector. The predicted target variable value has a predefined target event value only when the probability is greater than a predefined event cutoff value. (D) (A) through (C) are repeated. (E) An updated value is computed for the predefined event cutoff value. (F) (A) through (E) are repeated. An optimal event cutoff value is defined from the predefined event cutoff values used when repeating (A) through (E). The optimal value and prediction model are output.
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4.发明公开公开(公告)号：US20240193416A1
公开(公告)日：2024-06-13
申请号：US18444906
申请日：2024-02-19
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Ricky Dee Tharrington, JR. , Ralph Walter Abbey , Xin Jiang Hunt
IPC: G06N5/022
CPC classification number: G06N5/022
Abstract: A computing device trains a fair prediction model while defining an optimal event cutoff value. (A) A prediction model is trained with observation vectors. (B) The prediction model is executed to define a predicted target variable value and a probability associated with an accuracy of the predicted target variable value. (C) A conditional moments matrix is computed based on fairness constraints, the predicted target variable value, and the sensitive attribute variable value of each observation vector. The predicted target variable value has a predefined target event value only when the probability is greater than a predefined event cutoff value. (D) (A) through (C) are repeated. (E) An updated value is computed for the predefined event cutoff value. (F) (A) through (E) are repeated. An optimal event cutoff value is defined from the predefined event cutoff values used when repeating (A) through (E). The optimal value and prediction model are output.
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公开(公告)号：US11790036B2
公开(公告)日：2023-10-17
申请号：US18051906
申请日：2022-11-02
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Xin Jiang Hunt , Ralph Walter Abbey
Abstract: A computing device trains a fair machine learning model. A predicted target variable is defined using a trained prediction model. The prediction model is trained with weighted observation vectors. The predicted target variable is updated using the prediction model trained with weighted observation vectors. A true conditional moments matrix and a false conditional moments matrix are computed. The training and updating with weighted observation vectors are repeated until a number of iterations is performed. When a computed conditional moments matrix indicates to adjust a bound value, the bound value is updated based on an upper bound value or a lower bound value, and the repeated training and updating with weighted observation vectors is repeated with the bound value replaced with the updated bound value until the conditional moments matrix indicates no further adjustment of the bound value is needed. A fair prediction model is trained with the updated bound value.
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Patent Agency Ranking
公开(公告)号：US20230205839A1
公开(公告)日：2023-06-29
申请号：US18051906
申请日：2022-11-02
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Xin Jiang Hunt , Ralph Walter Abbey
Abstract: A computing device trains a fair machine learning model. A predicted target variable is defined using a trained prediction model. The prediction model is trained with weighted observation vectors. The predicted target variable is updated using the prediction model trained with weighted observation vectors. A true conditional moments matrix and a false conditional moments matrix are computed. The training and updating with weighted observation vectors are repeated until a number of iterations is performed. When a computed conditional moments matrix indicates to adjust a bound value, the bound value is updated based on an upper bound value or a lower bound value, and the repeated training and updating with weighted observation vectors is repeated with the bound value replaced with the updated bound value until the conditional moments matrix indicates no further adjustment of the bound value is needed. A fair prediction model is trained with the updated bound value.
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7.发明授权公开(公告)号：US12277224B1
公开(公告)日：2025-04-15
申请号：US19000677
申请日：2024-12-23
Applicant: SAS Institute Inc.
Inventor： Mohammadreza Nazari , Xindian Long , Steven Eric Krueger , Joshua David Griffin , Lawrence Edmund Lewis , Amirhassan Fallah Dizche , Ralph Walter Abbey , Jorge Manuel Gomes Da Silva
Abstract: A system, method, and computer-program product includes commencing a parent computer process based on receiving a request to perform an analytical operation on one or more datasets, commencing at least one child computer process that is launched by the parent computer process when the parent computer process initiates an execution of the analytical operation on the one or more datasets, transmitting, by the at least one child computer process, a request to the parent computer process to retrieve the one or more datasets, writing, by the parent computer process, the one or more datasets to a cross-process queue based on the parent computer process receiving the requests, reading, by the at least one child computer process, the one or more datasets from the cross-process queue, and executing, using an analytical application executing on the least one child computer process, the analytical operation based on the one or more datasets.
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公开(公告)号：US11922311B2
公开(公告)日：2024-03-05
申请号：US18208455
申请日：2023-06-12
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Ricky Dee Tharrington, Jr. , Ralph Walter Abbey
Abstract: A computing device trains a fair prediction model. A prediction model is trained and executed with observation vectors. A weight value is computed for each observation vector based on whether the predicted target variable value of a respective observation vector of the plurality of observation vectors has a predefined target event value. An observation vector is relabeled based on the computed weight value. The prediction model is retrained with each observation vector weighted by a respective computed weight value and with the target variable value of any observation vector that was relabeled. The retrained prediction model is executed. A conditional moments matrix is computed. A constraint violation matrix is computed. Computing the weight value through computing the constraint violation matrix is repeated until a stop criterion indicates retraining of the prediction model is complete. The retrained prediction model is output.
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公开(公告)号：US20230359890A1
公开(公告)日：2023-11-09
申请号：US18208455
申请日：2023-06-12
Applicant: SAS Institute Inc.
Inventor： Xinmin Wu , Ricky Dee Tharrington, JR. , Ralph Walter Abbey
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: A computing device trains a fair prediction model. A prediction model is trained and executed with observation vectors. A weight value is computed for each observation vector based on whether the predicted target variable value of a respective observation vector of the plurality of observation vectors has a predefined target event value. An observation vector is relabeled based on the computed weight value. The prediction model is retrained with each observation vector weighted by a respective computed weight value and with the target variable value of any observation vector that was relabeled. The retrained prediction model is executed. A conditional moments matrix is computed. A constraint violation matrix is computed. Computing the weight value through computing the constraint violation matrix is repeated until a stop criterion indicates retraining of the prediction model is complete. The retrained prediction model is output.
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公开(公告)号：US11531845B1
公开(公告)日：2022-12-20
申请号：US17837444
申请日：2022-06-10
Applicant: SAS Institute Inc.
Inventor： Xin Jiang Hunt , Xinmin Wu , Ralph Walter Abbey
Abstract: A computing device trains a fair machine learning model. A prediction model is trained to predict a target value. For a number of iterations, a weight vector is computed using the bound value based on fairness constraints defined for a fairness measure type; a weight value is assigned to each observation vector based on the target value and a sensitive attribute value; the prediction model is trained with each weighted observation vector to predict the target value; and a conditional moments vector is computed based on the fairness constraints and the target and sensitive attribute values. Conditional moments difference values are computed. When the conditional moments difference values indicate to adjust the bound value, the bound value is updated and the process is repeated with the bound value replaced with the updated bound value until the conditional moments difference values indicate no further adjustment of the bound value is needed.
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